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Wearable Sensor-Based Framework for the Detection of Daily Living Activities Utilizing In-Depth Features

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Machine Learning, Image Processing, Network Security and Data Sciences

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 946))

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Abstract

This paper implements the physical activity recognition model on 13 (sitting, standing, ironing, etc.) daily living activities using a 2D convolutional neural network (CNN). The model has outperformed the existing methods for the PAMAP2 and WISDM datasets. The various machine learning models are also implemented. These are SVM, Naïve Bayes, decision tree, and random forest. Average accuracy, F1 score, precision, and recall are the performance metrics used to evaluate the results. It is found in overall findings that convolutional neural networks have detected all the activities very precisely and accurately where machine learning models felt difficulty in classifying more accurately a particular activity. This difficulty of these machine learning models is addressed in this research by introducing CNN features in the training of machine learning models. Mean accuracy of 99.13 and 99.18% is achieved with convolutional neural network and Naïve Bayes with CNN features, respectively, for the PAMAP2 dataset. Mean accuracy of 98.01 and 98.09% is achieved with convolutional neural network and Naïve Bayes with CNN features, respectively, for the WISDM dataset.

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Authors and Affiliations

  1. Sant Longowal Institute of Engineering and Technology, Punjab, India

    Updesh Verma, Pratibha Tyagi & Manpreet Kaur

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  1. Updesh Verma
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  2. Pratibha Tyagi
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  3. Manpreet Kaur
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Correspondence to Updesh Verma .

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Editors and Affiliations

  1. Department of Information Technology, National Institute of Technology Raipur, Raipur, Chhattisgarh, India

    Rajesh Doriya

  2. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, India

    Badal Soni

  3. Indian Institute of Information Technology, Pune, India

    Anupam Shukla

  4. Faculty of Science and Forestry, School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

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Verma, U., Tyagi, P., Kaur, M. (2023). Wearable Sensor-Based Framework for the Detection of Daily Living Activities Utilizing In-Depth Features. In: Doriya, R., Soni, B., Shukla, A., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. Lecture Notes in Electrical Engineering, vol 946. Springer, Singapore. https://doi.org/10.1007/978-981-19-5868-7_11

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  • DOI: https://doi.org/10.1007/978-981-19-5868-7_11

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  • Print ISBN: 978-981-19-5867-0

  • Online ISBN: 978-981-19-5868-7

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